通过显式学习语义差异，提升遥感变化检测的鲁棒性
LDGuid: A Framework for Robust Change Detection via Latent Difference Guidance
- 用对抗自编码器构建差异嵌入模块，只学任务相关差异
- 在多个数据集上显著提升分割性能，尤其抗光谱噪声能力强
- 适合需要引入领域知识（如光谱指数）的变化检测场景
当前深度学习变化检测模型难以显式表征与任务相关的语义差异。本文提出潜空间差异引导框架（LDGuid），通过对抗自编码器实现差异嵌入（DE）模块，并利用信息瓶颈方法预训练，使其仅学习前后时相样本间的任务相关差异。该学习到的潜在差异作为显式引导信号注入变化检测模型。我们将在U-Net、BIT和AERNet等基线中集成LDGuid，并在LEVIR-CD、WHU-CD、SVCD和CaBuAr数据集上评估。实验表明，LDGuid在所有基准上均提升分割性能，尤其在受光谱噪声影响的复杂场景中表现突出。结果还显示，该框架能有效融入领域知识，如特定任务的光谱指数。研究证明，语义差异学习可显著增强遥感变化检测的鲁棒性。
原文摘要 · Abstract (English)
Modern deep learning models for change detection (CD) often struggle to explicitly represent task-relevant semantic differences. This paper proposes the Latent Difference Guidance (LDGuid) framework that explicitly learns and injects semantic differences into CD models. LDGuid deploys adversarial autoencoding to implement a difference embedding (DE) module. The DE module is pretrained via the information bottleneck method, restricting it to learn only task-relevant differences between pre- and post-event samples. The learned latent difference is then used as an explicit guidance signal in the CD model. We validate LDGuid by integrating it into U-Net, BIT, and AERNet baselines for CD and evaluating it on LEVIR-CD, WHU-CD, SVCD, and CaBuAr datasets. Experimental results show that LDGuid enhances segmentation performance across all benchmarks, with particularly remarkable gains in challenging settings affected by spectral noise. The results further highlight the ability of LDGuid in incorporating domain knowledge, such as task-specific spectral indices. Our findings suggest that semantic difference learning can drastically enhance the robustness of CD in remote sensing.
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